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Communication Dans Un Congrès Année : 2011

Toward error-bounded algorithms for infinite-horizon DEC-POMDPs

Résumé

Over the past few years, attempts to scale up infinite-horizon DECPOMDPs are mainly due to approximate algorithms, but without the theoretical guarantees of their exact counterparts. In contrast, "-optimal methods have only theoretical significance but are not efficient in practice. In this paper, we introduce an algorithmic framework ( -PI) that exploits the scalability of the former while preserving the theoretical properties of the latter. We build upon -PI a family of approximate algorithms that can find (provably) errorbounded solutions in reasonable time. Among this family, H-PI uses a branch-and-bound search method that computes a near-optimal solution over distributions over histories experienced by the agents. These distributions often lie near structured, low-dimensional subspace embedded in the high-dimensional sufficient statistic. By planning only on this subspace, H-PI successfully solves all tested benchmarks, outperforming standard algorithms, both in solution time and policy quality.
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hal-00969579 , version 1 (04-04-2014)

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  • HAL Id : hal-00969579 , version 1

Citer

Jilles Steeve Dibangoye, Abdel-Illah Mouaddib, Brahim Chaïb-Draa. Toward error-bounded algorithms for infinite-horizon DEC-POMDPs. AAMAS, 2011, France. pp.947-954. ⟨hal-00969579⟩
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